Implementation Path: AI development organizations should experiment with novel governance and oversight structures that let a broader set of stakeholders factor into model deployment decisions. This could take the form of oversight functions which can critique and publicly censure organizations should the organization diverge from the recommendations of the oversight body, to novel forms of governance that give diverse stakeholders power over an organization (for example, a private company could elect board members who represent the interests of civil society and/or academia rather than a pure profit-driven motive). AI development organizations should also work among themselves to develop best practices for the development and deployment of AI systems, then seek to get feedback on these from a broader range of stakeholders, potentially via the creation of third-party organizations for the purposes of standard formation. Along with innovations in governance of AI organizations, and work on best practices, we also believe governments should invest in better methods to assure the benefits of systems being deployed - specifically, governments should support efforts to measure and monitor the capabilities (both harmful and beneficial) of deployed AI systems [1]}, and should support the creation of an ecosystem oriented around auditing AI models and AI development processes [2]}, [3]}, [4]}.
